Overview

Dataset statistics

Number of variables14
Number of observations503
Missing cells1414
Missing cells (%)20.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.1 KiB
Average record size in memory112.3 B

Variable types

DateTime1
Numeric13

Warnings

Total Pasien is highly correlated with Sembuh and 8 other fieldsHigh correlation
Sembuh is highly correlated with Total Pasien and 6 other fieldsHigh correlation
Meninggal is highly correlated with Total Pasien and 8 other fieldsHigh correlation
Self Isolation is highly correlated with Total Pasien and 9 other fieldsHigh correlation
Masih Perawatan is highly correlated with Total Pasien and 10 other fieldsHigh correlation
Menunggu Hasil is highly correlated with Total Pasien and 7 other fieldsHigh correlation
Tenaga Kesehatan Terinfeksi is highly correlated with Total Pasien and 7 other fieldsHigh correlation
Positif Harian is highly correlated with Total Pasien and 10 other fieldsHigh correlation
Positif Aktif is highly correlated with Total Pasien and 10 other fieldsHigh correlation
Sembuh Harian is highly correlated with Total Pasien and 7 other fieldsHigh correlation
Tanpa Gejala is highly correlated with BergejalaHigh correlation
Bergejala is highly correlated with Self Isolation and 6 other fieldsHigh correlation
Belum Ada Data is highly correlated with Self Isolation and 5 other fieldsHigh correlation
Total Pasien is highly correlated with Sembuh and 9 other fieldsHigh correlation
Sembuh is highly correlated with Total Pasien and 9 other fieldsHigh correlation
Meninggal is highly correlated with Total Pasien and 9 other fieldsHigh correlation
Self Isolation is highly correlated with Total Pasien and 10 other fieldsHigh correlation
Masih Perawatan is highly correlated with Total Pasien and 9 other fieldsHigh correlation
Menunggu Hasil is highly correlated with Total Pasien and 7 other fieldsHigh correlation
Tenaga Kesehatan Terinfeksi is highly correlated with Total Pasien and 7 other fieldsHigh correlation
Positif Harian is highly correlated with Total Pasien and 8 other fieldsHigh correlation
Positif Aktif is highly correlated with Total Pasien and 9 other fieldsHigh correlation
Sembuh Harian is highly correlated with Total Pasien and 6 other fieldsHigh correlation
Tanpa Gejala is highly correlated with Total Pasien and 4 other fieldsHigh correlation
Bergejala is highly correlated with Self Isolation and 3 other fieldsHigh correlation
Total Pasien is highly correlated with Sembuh and 7 other fieldsHigh correlation
Sembuh is highly correlated with Total Pasien and 7 other fieldsHigh correlation
Meninggal is highly correlated with Total Pasien and 7 other fieldsHigh correlation
Self Isolation is highly correlated with Masih Perawatan and 5 other fieldsHigh correlation
Masih Perawatan is highly correlated with Total Pasien and 8 other fieldsHigh correlation
Menunggu Hasil is highly correlated with Total Pasien and 7 other fieldsHigh correlation
Tenaga Kesehatan Terinfeksi is highly correlated with Total Pasien and 7 other fieldsHigh correlation
Positif Harian is highly correlated with Total Pasien and 8 other fieldsHigh correlation
Positif Aktif is highly correlated with Total Pasien and 8 other fieldsHigh correlation
Sembuh Harian is highly correlated with Total Pasien and 6 other fieldsHigh correlation
Positif Aktif is highly correlated with Belum Ada Data and 9 other fieldsHigh correlation
Belum Ada Data is highly correlated with Positif Aktif and 9 other fieldsHigh correlation
Masih Perawatan is highly correlated with Positif Aktif and 9 other fieldsHigh correlation
Menunggu Hasil is highly correlated with Tenaga Kesehatan TerinfeksiHigh correlation
Total Pasien is highly correlated with Positif Aktif and 9 other fieldsHigh correlation
Tenaga Kesehatan Terinfeksi is highly correlated with Menunggu HasilHigh correlation
Meninggal is highly correlated with Positif Aktif and 9 other fieldsHigh correlation
Tanpa Gejala is highly correlated with Positif Aktif and 8 other fieldsHigh correlation
Positif Harian is highly correlated with Positif Aktif and 9 other fieldsHigh correlation
Sembuh Harian is highly correlated with Positif Aktif and 8 other fieldsHigh correlation
Sembuh is highly correlated with Positif Aktif and 9 other fieldsHigh correlation
Bergejala is highly correlated with Positif Aktif and 9 other fieldsHigh correlation
Self Isolation is highly correlated with Positif Aktif and 9 other fieldsHigh correlation
Menunggu Hasil has 497 (98.8%) missing values Missing
Tenaga Kesehatan Terinfeksi has 497 (98.8%) missing values Missing
Tanpa Gejala has 140 (27.8%) missing values Missing
Bergejala has 140 (27.8%) missing values Missing
Belum Ada Data has 140 (27.8%) missing values Missing
Tanggal Jam has unique values Unique
Sembuh has 17 (3.4%) zeros Zeros
Self Isolation has 15 (3.0%) zeros Zeros
Positif Harian has 7 (1.4%) zeros Zeros
Sembuh Harian has 24 (4.8%) zeros Zeros

Reproduction

Analysis started2021-07-17 04:05:39.591575
Analysis finished2021-07-17 04:05:58.418943
Duration18.83 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Tanggal Jam
Date

UNIQUE

Distinct503
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Minimum2020-03-01 18:00:00
Maximum2021-07-16 08:00:00
2021-07-17T11:05:58.485003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:58.587096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Total Pasien
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct497
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180943.3161
Minimum0
Maximum727016
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-07-17T11:05:58.694193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile520.1
Q112167
median111201
Q3358259
95-th percentile478541.6
Maximum727016
Range727016
Interquartile range (IQR)346092

Descriptive statistics

Standard deviation182803.7804
Coefficient of variation (CV)1.010282028
Kurtosis-0.6148424024
Mean180943.3161
Median Absolute Deviation (MAD)106426
Skewness0.7399923789
Sum91014488
Variance3.341722212 × 1010
MonotonicityIncreasing
2021-07-17T11:05:58.802796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74
 
0.8%
02
 
0.4%
32
 
0.4%
342
 
0.4%
37461
 
0.2%
1112011
 
0.2%
252421
 
0.2%
1368611
 
0.2%
88631
 
0.2%
1512011
 
0.2%
Other values (487)487
96.8%
ValueCountFrequency (%)
02
0.4%
32
0.4%
74
0.8%
342
0.4%
361
 
0.2%
621
 
0.2%
721
 
0.2%
791
 
0.2%
951
 
0.2%
971
 
0.2%
ValueCountFrequency (%)
7270161
0.2%
7146011
0.2%
7019101
0.2%
6892431
0.2%
6770611
0.2%
6624421
0.2%
6493091
0.2%
6363891
0.2%
6232771
0.2%
6103031
0.2%

Sembuh
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct480
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165366.002
Minimum0
Maximum604034
Zeros17
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-07-17T11:05:58.920904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.2
Q17520
median100816
Q3344276.5
95-th percentile438704.5
Maximum604034
Range604034
Interquartile range (IQR)336756.5

Descriptive statistics

Standard deviation170233.3244
Coefficient of variation (CV)1.029433634
Kurtosis-1.060677193
Mean165366.002
Median Absolute Deviation (MAD)100013
Skewness0.6323529223
Sum83179099
Variance2.897938474 × 1010
MonotonicityIncreasing
2021-07-17T11:05:59.030003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017
 
3.4%
1423
 
0.6%
492
 
0.4%
3382
 
0.4%
132
 
0.4%
822
 
0.4%
4122
 
0.4%
3846521
 
0.2%
98571
 
0.2%
6501
 
0.2%
Other values (470)470
93.4%
ValueCountFrequency (%)
017
3.4%
121
 
0.2%
132
 
0.4%
171
 
0.2%
211
 
0.2%
221
 
0.2%
231
 
0.2%
271
 
0.2%
291
 
0.2%
311
 
0.2%
ValueCountFrequency (%)
6040341
0.2%
5955821
0.2%
5925561
0.2%
5894861
0.2%
5849121
0.2%
5644371
0.2%
5438671
0.2%
5269411
0.2%
5120851
0.2%
5011991
0.2%

Meninggal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct488
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3182.359841
Minimum0
Maximum9845
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-07-17T11:05:59.151114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.8
Q1654
median2359
Q35980
95-th percentile7968.9
Maximum9845
Range9845
Interquartile range (IQR)5326

Descriptive statistics

Standard deviation2766.950344
Coefficient of variation (CV)0.8694649513
Kurtosis-1.006511097
Mean3182.359841
Median Absolute Deviation (MAD)1858
Skewness0.5976580609
Sum1600727
Variance7656014.207
MonotonicityIncreasing
2021-07-17T11:05:59.265217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37
 
1.4%
02
 
0.4%
122
 
0.4%
6582
 
0.4%
292
 
0.4%
6322
 
0.4%
3812
 
0.4%
69732
 
0.4%
12
 
0.4%
8522
 
0.4%
Other values (478)478
95.0%
ValueCountFrequency (%)
02
 
0.4%
12
 
0.4%
37
1.4%
51
 
0.2%
71
 
0.2%
91
 
0.2%
111
 
0.2%
122
 
0.4%
151
 
0.2%
191
 
0.2%
ValueCountFrequency (%)
98451
0.2%
97431
0.2%
96031
0.2%
95411
0.2%
94621
0.2%
93951
0.2%
93571
0.2%
93061
0.2%
91101
0.2%
90421
0.2%

Self Isolation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct479
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8424.180915
Minimum0
Maximum88294
Zeros15
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-07-17T11:05:59.384325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile114.9
Q13013.5
median5028
Q39005.5
95-th percentile21188.6
Maximum88294
Range88294
Interquartile range (IQR)5992

Descriptive statistics

Standard deviation12549.08667
Coefficient of variation (CV)1.489650661
Kurtosis17.49156127
Mean8424.180915
Median Absolute Deviation (MAD)2500
Skewness3.98638959
Sum4237363
Variance157479576.2
MonotonicityNot monotonic
2021-07-17T11:05:59.487419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015
 
3.0%
1512
 
0.4%
34832
 
0.4%
33662
 
0.4%
51612
 
0.4%
86432
 
0.4%
49932
 
0.4%
35842
 
0.4%
34852
 
0.4%
28222
 
0.4%
Other values (469)470
93.4%
ValueCountFrequency (%)
015
3.0%
71
 
0.2%
271
 
0.2%
421
 
0.2%
571
 
0.2%
661
 
0.2%
711
 
0.2%
771
 
0.2%
791
 
0.2%
1091
 
0.2%
ValueCountFrequency (%)
882941
0.2%
862981
0.2%
785711
0.2%
732391
0.2%
723611
0.2%
718481
0.2%
718121
0.2%
696441
0.2%
682511
0.2%
665811
0.2%

Masih Perawatan
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct479
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3970.77336
Minimum0
Maximum30418
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-07-17T11:05:59.601522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile326.2
Q11872
median2861
Q34407
95-th percentile11035.7
Maximum30418
Range30418
Interquartile range (IQR)2535

Descriptive statistics

Standard deviation4675.787035
Coefficient of variation (CV)1.177550722
Kurtosis14.12603338
Mean3970.77336
Median Absolute Deviation (MAD)1162
Skewness3.58228591
Sum1997299
Variance21862984.39
MonotonicityNot monotonic
2021-07-17T11:05:59.714625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44
 
0.8%
28613
 
0.6%
25553
 
0.6%
25592
 
0.4%
4952
 
0.4%
22582
 
0.4%
30152
 
0.4%
20102
 
0.4%
56202
 
0.4%
6912
 
0.4%
Other values (469)479
95.2%
ValueCountFrequency (%)
02
0.4%
22
0.4%
44
0.8%
312
0.4%
331
 
0.2%
571
 
0.2%
651
 
0.2%
701
 
0.2%
781
 
0.2%
831
 
0.2%
ValueCountFrequency (%)
304181
0.2%
297211
0.2%
291361
0.2%
282901
0.2%
276871
0.2%
274421
0.2%
269031
0.2%
253801
0.2%
248431
0.2%
242731
0.2%

Menunggu Hasil
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)100.0%
Missing497
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean411.3333333
Minimum345
Maximum479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-07-17T11:05:59.813715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum345
5-th percentile352.5
Q1379.5
median412
Q3441.5
95-th percentile470.5
Maximum479
Range134
Interquartile range (IQR)62

Descriptive statistics

Standard deviation49.3058482
Coefficient of variation (CV)0.1198683506
Kurtosis-1.084034017
Mean411.3333333
Median Absolute Deviation (MAD)35
Skewness0.0240179704
Sum2468
Variance2431.066667
MonotonicityStrictly increasing
2021-07-17T11:05:59.900795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4451
 
0.2%
3931
 
0.2%
4791
 
0.2%
4311
 
0.2%
3751
 
0.2%
3451
 
0.2%
(Missing)497
98.8%
ValueCountFrequency (%)
3451
0.2%
3751
0.2%
3931
0.2%
4311
0.2%
4451
0.2%
4791
0.2%
ValueCountFrequency (%)
4791
0.2%
4451
0.2%
4311
0.2%
3931
0.2%
3751
0.2%
3451
0.2%

Tenaga Kesehatan Terinfeksi
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)100.0%
Missing497
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean37.16666667
Minimum25
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-07-17T11:05:59.991877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile25.75
Q130.25
median39.5
Q343.5
95-th percentile46.25
Maximum47
Range22
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation8.931218655
Coefficient of variation (CV)0.2403018472
Kurtosis-1.733980343
Mean37.16666667
Median Absolute Deviation (MAD)6
Skewness-0.5004583891
Sum223
Variance79.76666667
MonotonicityStrictly increasing
2021-07-17T11:06:00.069948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
441
 
0.2%
471
 
0.2%
421
 
0.2%
281
 
0.2%
251
 
0.2%
371
 
0.2%
(Missing)497
98.8%
ValueCountFrequency (%)
251
0.2%
281
0.2%
371
0.2%
421
0.2%
441
0.2%
471
0.2%
ValueCountFrequency (%)
471
0.2%
441
0.2%
421
0.2%
371
0.2%
281
0.2%
251
0.2%

Positif Harian
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct437
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1445.359841
Minimum0
Maximum14619
Zeros7
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-07-17T11:06:00.173042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46.4
Q1219
median890
Q31447.5
95-th percentile4879.2
Maximum14619
Range14619
Interquartile range (IQR)1228.5

Descriptive statistics

Standard deviation2245.317226
Coefficient of variation (CV)1.5534659
Kurtosis13.76228417
Mean1445.359841
Median Absolute Deviation (MAD)634
Skewness3.537912647
Sum727016
Variance5041449.446
MonotonicityNot monotonic
2021-07-17T11:06:00.284142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07
 
1.4%
1274
 
0.8%
763
 
0.6%
983
 
0.6%
8672
 
0.4%
1472
 
0.4%
11652
 
0.4%
932
 
0.4%
1242
 
0.4%
1202
 
0.4%
Other values (427)474
94.2%
ValueCountFrequency (%)
07
1.4%
22
 
0.4%
31
 
0.2%
41
 
0.2%
71
 
0.2%
101
 
0.2%
142
 
0.4%
161
 
0.2%
251
 
0.2%
262
 
0.4%
ValueCountFrequency (%)
146191
0.2%
131331
0.2%
131121
0.2%
129741
0.2%
129201
0.2%
126911
0.2%
126671
0.2%
124151
0.2%
121821
0.2%
109031
0.2%

Positif Aktif
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct491
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12394.95427
Minimum0
Maximum113137
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-07-17T11:06:00.406305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile441.1
Q14512
median7816
Q312931
95-th percentile31868.2
Maximum113137
Range113137
Interquartile range (IQR)8419

Descriptive statistics

Standard deviation16912.61419
Coefficient of variation (CV)1.364475722
Kurtosis16.32496647
Mean12394.95427
Median Absolute Deviation (MAD)3695
Skewness3.903331825
Sum6234662
Variance286036518.7
MonotonicityNot monotonic
2021-07-17T11:06:00.512553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44
 
0.8%
02
 
0.4%
312
 
0.4%
70272
 
0.4%
131402
 
0.4%
41492
 
0.4%
89252
 
0.4%
72262
 
0.4%
62712
 
0.4%
22
 
0.4%
Other values (481)481
95.6%
ValueCountFrequency (%)
02
0.4%
22
0.4%
44
0.8%
312
0.4%
331
 
0.2%
571
 
0.2%
651
 
0.2%
701
 
0.2%
841
 
0.2%
851
 
0.2%
ValueCountFrequency (%)
1131371
0.2%
1092761
0.2%
1020821
0.2%
1001421
0.2%
1000621
0.2%
997511
0.2%
960851
0.2%
945841
0.2%
911631
0.2%
902161
0.2%

Sembuh Harian
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct410
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1200.862823
Minimum0
Maximum20570
Zeros24
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-07-17T11:06:00.629731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1162
median864
Q31318
95-th percentile3516.6
Maximum20570
Range20570
Interquartile range (IQR)1156

Descriptive statistics

Standard deviation1968.090811
Coefficient of variation (CV)1.638897277
Kurtosis49.49797113
Mean1200.862823
Median Absolute Deviation (MAD)648
Skewness6.053271313
Sum604034
Variance3873381.441
MonotonicityNot monotonic
2021-07-17T11:06:01.117174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024
 
4.8%
19
 
1.8%
27
 
1.4%
46
 
1.2%
64
 
0.8%
74
 
0.8%
34
 
0.8%
1073
 
0.6%
11243
 
0.6%
1783
 
0.6%
Other values (400)436
86.7%
ValueCountFrequency (%)
024
4.8%
19
 
1.8%
27
 
1.4%
34
 
0.8%
46
 
1.2%
53
 
0.6%
64
 
0.8%
74
 
0.8%
81
 
0.2%
122
 
0.4%
ValueCountFrequency (%)
205701
0.2%
204751
0.2%
169261
0.2%
148561
0.2%
108861
0.2%
84521
0.2%
66071
0.2%
59361
0.2%
57991
0.2%
57571
0.2%

Tanpa Gejala
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct350
Distinct (%)96.4%
Missing140
Missing (%)27.8%
Infinite0
Infinite (%)0.0%
Mean6415.278237
Minimum75
Maximum32752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-07-17T11:06:01.233279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile697.8
Q11678.5
median5016
Q37732.5
95-th percentile20500
Maximum32752
Range32677
Interquartile range (IQR)6054

Descriptive statistics

Standard deviation6415.994815
Coefficient of variation (CV)1.000111699
Kurtosis3.416622348
Mean6415.278237
Median Absolute Deviation (MAD)3149
Skewness1.851960802
Sum2328746
Variance41164989.47
MonotonicityNot monotonic
2021-07-17T11:06:01.340377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62783
 
0.6%
205003
 
0.6%
54102
 
0.4%
26222
 
0.4%
68202
 
0.4%
153242
 
0.4%
93482
 
0.4%
11952
 
0.4%
260052
 
0.4%
87702
 
0.4%
Other values (340)341
67.8%
(Missing)140
27.8%
ValueCountFrequency (%)
751
0.2%
1611
0.2%
1771
0.2%
2461
0.2%
3051
0.2%
3101
0.2%
3761
0.2%
3841
0.2%
4431
0.2%
5361
0.2%
ValueCountFrequency (%)
327521
0.2%
319221
0.2%
295531
0.2%
292531
0.2%
289021
0.2%
285021
0.2%
282161
0.2%
275021
0.2%
260052
0.4%
256001
0.2%

Bergejala
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct347
Distinct (%)95.6%
Missing140
Missing (%)27.8%
Infinite0
Infinite (%)0.0%
Mean8437.865014
Minimum637
Maximum60476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-07-17T11:06:01.454481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum637
5-th percentile3184.9
Q14478
median6139
Q39827
95-th percentile19726.7
Maximum60476
Range59839
Interquartile range (IQR)5349

Descriptive statistics

Standard deviation7775.738494
Coefficient of variation (CV)0.9215291405
Kurtosis19.48499986
Mean8437.865014
Median Absolute Deviation (MAD)1997
Skewness3.932793223
Sum3062945
Variance60462109.12
MonotonicityNot monotonic
2021-07-17T11:06:01.566583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53013
 
0.6%
44783
 
0.6%
47492
 
0.4%
80772
 
0.4%
62832
 
0.4%
89162
 
0.4%
66212
 
0.4%
101022
 
0.4%
120272
 
0.4%
41422
 
0.4%
Other values (337)341
67.8%
(Missing)140
27.8%
ValueCountFrequency (%)
6371
0.2%
7451
0.2%
13061
0.2%
14811
0.2%
17491
0.2%
19472
0.4%
21381
0.2%
21911
0.2%
26141
0.2%
26381
0.2%
ValueCountFrequency (%)
604761
0.2%
575211
0.2%
544631
0.2%
523601
0.2%
508241
0.2%
481441
0.2%
407271
0.2%
294611
0.2%
289251
0.2%
275141
0.2%

Belum Ada Data
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct352
Distinct (%)97.0%
Missing140
Missing (%)27.8%
Infinite0
Infinite (%)0.0%
Mean4836.107438
Minimum0
Maximum70948
Zeros3
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2021-07-17T11:06:01.689695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile472.2
Q1908
median2023
Q34174
95-th percentile21171.1
Maximum70948
Range70948
Interquartile range (IQR)3266

Descriptive statistics

Standard deviation9643.271699
Coefficient of variation (CV)1.994015192
Kurtosis19.96673078
Mean4836.107438
Median Absolute Deviation (MAD)1271
Skewness4.284671332
Sum1755507
Variance92992689.07
MonotonicityNot monotonic
2021-07-17T11:06:01.800795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7443
 
0.6%
9673
 
0.6%
03
 
0.6%
8652
 
0.4%
6332
 
0.4%
13532
 
0.4%
11982
 
0.4%
15092
 
0.4%
14431
 
0.2%
78941
 
0.2%
Other values (342)342
68.0%
(Missing)140
27.8%
ValueCountFrequency (%)
03
0.6%
61
 
0.2%
1981
 
0.2%
2241
 
0.2%
2341
 
0.2%
2741
 
0.2%
3371
 
0.2%
3531
 
0.2%
3721
 
0.2%
3821
 
0.2%
ValueCountFrequency (%)
709481
0.2%
634691
0.2%
617071
0.2%
580941
0.2%
509201
0.2%
493201
0.2%
491321
0.2%
412151
0.2%
400221
0.2%
394411
0.2%

Interactions

2021-07-17T11:05:39.846807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:39.967917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:40.082525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:40.195628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:40.310733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:40.428840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:40.520924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:40.601998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:40.722107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:40.837211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:40.946310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:41.058412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-07-17T11:05:55.832583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:55.936678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:56.033766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:56.136859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:56.239954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:56.348557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:56.460659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:56.568757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:56.671851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:56.776947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:56.885045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:56.966118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:57.049193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:57.162297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:57.267392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:57.367483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:57.477583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-17T11:05:57.585681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-07-17T11:06:01.922906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-17T11:06:02.132097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-17T11:06:02.344290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-17T11:06:02.554480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-07-17T11:05:57.783861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-17T11:05:58.027587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-07-17T11:05:58.191736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-07-17T11:05:58.315849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Tanggal JamTotal PasienSembuhMeninggalSelf IsolationMasih PerawatanMenunggu HasilTenaga Kesehatan TerinfeksiPositif HarianPositif AktifSembuh HarianTanpa GejalaBergejalaBelum Ada Data
02020-03-01 18:00:0000000NaNNaN000NaNNaNNaN
12020-03-02 18:00:0000000NaNNaN000NaNNaNNaN
22020-03-03 18:00:0030102NaNNaN320NaNNaNNaN
32020-03-04 18:00:0030102NaNNaN020NaNNaNNaN
42020-03-05 18:00:0070304NaNNaN440NaNNaNNaN
52020-03-06 18:00:0070304NaNNaN040NaNNaNNaN
62020-03-07 18:00:0070304NaNNaN040NaNNaNNaN
72020-03-08 18:00:0070304NaNNaN040NaNNaNNaN
82020-03-09 18:00:003403031NaNNaN27310NaNNaNNaN
92020-03-10 18:00:003403031NaNNaN0310NaNNaNNaN

Last rows

Tanggal JamTotal PasienSembuhMeninggalSelf IsolationMasih PerawatanMenunggu HasilTenaga Kesehatan TerinfeksiPositif HarianPositif AktifSembuh HarianTanpa GejalaBergejalaBelum Ada Data
4932021-07-07 08:00:0061030350119990426964430418NaNNaN9366100062370718376.057521.024165.0
4942021-07-08 08:00:0062327751208591107236129721NaNNaN129741020821088614748.050824.036510.0
4952021-07-09 08:00:0063638952694193067323926903NaNNaN131121001421485610095.040727.049320.0
4962021-07-10 08:00:0064930954386793577181224273NaNNaN1292096085169267999.026379.061707.0
4972021-07-11 08:00:0066244256443793956825120359NaNNaN1313388610205704769.012893.070948.0
4982021-07-12 08:00:0067706158491294626658116106NaNNaN14619826872047517649.025597.039441.0
4992021-07-13 08:00:0068924358948695417184818368NaNNaN1218290216457416957.022339.050920.0
5002021-07-14 08:00:0070191059255696037857121180NaNNaN1266799751307032752.054463.012536.0
5012021-07-15 08:00:0071460159558297438629822978NaNNaN12691109276302631922.052360.024994.0
5022021-07-16 08:00:0072701660403498458829424843NaNNaN12415113137845228216.048144.036777.0